Senior Data Scientist II

Do you love collaborating with teams to solve complex problems and deliver solutions? Would you like to partner with the biggest names in the insurance industry?

About the business:

LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Insurance vertical, we provide customers with solutions and decision tools that combine public and industry specific content with advanced technology and analytics to assist them in evaluating and predicting risk and enhancing operational efficiency. Our insurance risk solutions help drive better data-driven decisions across the insurance policy lifecycle – all while reducing risk. You can learn more about LexisNexis Risk at the link below. https://risk.lexisnexis.com/insurance

About the team:

The Property Analytics team is dedicated to harnessing the power of data and sophisticated analysis to assess risks and enhance decision making for insurance carriers. At LexisNexis Risk Solutions, we pride ourselves on

employing advanced statistical models and predictive algorithms to provide valuable insights that enable insurance providers to make informed decisions.

About the job:

As a Senior Data Scientist II, lead and manage complex data science projects. Use your expertise to develop statistical models and algorithms for data-driven decision-making. Improve customer experiences through these efforts.

You will be responsible for:

• Contributing to the development of best practices for data science at the company
• Analyzing organizational data to recommend solutions to new and complex problems.
• Developing and optimizing machine learning algorithms for predictive modeling
• Developing and utilizing innovative strategies to complete business analysis and evaluate the
• performance of business segments.
• Exploring and mining new data sources to help optimize and validate existing and new products.
• All other duties as assigned.

Qualifications:

• Graduate degree (Masters or PhD) in Data Science, Statistics, Engineering, Computer Science, or a Quantitative field
• 3-5 yrs. of hands-on statistical model development/machine learning (ML) experience. Previous insurance experience preferred.
• Solid understanding of ML techniques including hypothesis testing, sample design, model development (linear and non-linear models), validation of machine learning models.
• Strong programming skills in Python, with extensive experience with their standard data manipulation and ML packages. (pandas, scikit-learn, NumPy, XGBoost, PyTorch in Python)
• Strong verbal and written communications skills and is comfortable presenting and explaining results to business stakeholders.
• Strong ability as a self-starter to learn new technologies (Pyspark, ECL, Azure/AWS ML Services) and to share cross-functional knowledge across the teams.
• Experience in data management and data analysis in on-premise and cloud database management systems (like SQL Server, Cosmos DB, Blob storage, etc.)

Culture and Benefits:

Learn more about the LexisNexis Risk team and how we work here.

At LexisNexis Risk Solutions, having diverse employees with different perspectives is key to creating innovative new products for our global customers. We have 30 diversity employee networks globally and prioritize inclusive leadership and equitable processes as part of our culture. Our aim is for every employee to be the best version of themselves. We would actively welcome applications from candidates of diverse backgrounds and underrepresented groups.

We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law. We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form: https://forms.office.com/r/eVgFxjLmAK .

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